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Record W2164854053 · doi:10.1109/lawp.2015.2419174

A Vertical Reflection Ionospheric Clutter Model for HF Radar Used in Coastal Remote Sensing

2015· article· en· W2164854053 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsIonosphereClutterIonospheric reflectionRadarReflection (computer programming)Radar horizonGeologyDoppler effectRemote sensingPhysicsIonospheric heaterContinuous-wave radarGeophysicsRadar imagingComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A theoretical model of the high frequency (HF) radar ionospheric clutter for the case of vertical incidence is presented in this letter. Assuming a pulsed dipole source, the equation of the received electric field after vertical reflection by the ionosphere is first derived. Then the relationship between the ionospheric reflection coefficient and the ionospheric electron density is incorporated into the development of the ionospheric clutter power model. In order to investigate the power spectrum of this ionospheric clutter and its relative intensity to that of the first-order ocean clutter, the normalized ionospheric clutter power is simulated. Simulation results show that the horizontal ionospheric plasma drift velocity results in a Doppler spreading while the vertical motion introduces a simple Doppler shift.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.245
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it